Learn embeddings, ANN search, and vector DBs like FAISS, Pinecone & Chroma to build real AI search, RAG pipelines, apps.

What you'll learn
- Understand the mathematical foundations of vector search (linear algebra, probability, ANN optimization).
- Generate, evaluate, and work with embeddings using tools like OpenAI, Hugging Face, and sentence-transformers.
- Explain how vector databases differ from traditional databases.
- Build and query vector indexes using FAISS, Pinecone, Chroma, and Weaviate.
- Implement Approximate Nearest Neighbor (ANN) search and compare index types.
- Build a semantic search system from scratch using embeddings + vector DB.
- Design and deploy RAG (Retrieval-Augmented Generation) pipelines with LLMs.
- Compare performance, scalability, and cost of major vector databases.
- Integrate vector databases with LangChain, Python, and LLM APIs.
- Build production-ready applications such as chatbots, knowledge bases, and recommendation engines.
- Optimize query latency, memory usage, and indexing strategies for high-dimensional data.
- Understand real-world deployment challenges and best practices.

Requirements
- Enthusiasm and determination to make your mark on the world!

Description
A warm welcome toVector Databases in Action: FAISS, Pinecone, Chroma & Weaviatecourse byUplatz.

What Are Vector Databases?Vector databasesare specialized data systems designed to store and search high-dimensional vectors — numerical representations of data such as text, images, audio, or code. These vectors (embeddings) capture semantic meaning, allowing machines to compare similarity between items using distance metrics like cosine similarity. Unlike traditional databases that search by exact matches or SQL filters, vector databases enablesemantic retrieval, powering AI applications such as chatbots, recommendation engines, RAG pipelines, document search, and multimodal understanding.

How They WorkWhen data is converted into embeddings (vectors), these are stored in an index optimized for fast Approximate Nearest Neighbor (ANN) search. During a query, the user input is also transformed into a vector, and the database retrievesthe most similar vectorsbased on distance calculations. Various indexing algorithms (e.g., HNSW, IVF, PQ) allow sub-second responses even with millions of vectors. Vector databases can also combine keyword filtering, metadata search, and semantic search for hybrid querying — making them ideal for production-grade AI systems.

Popular Vector DatabasesThis course dives deep into the four most widely used vector databases.

1.FAISS, developed by Facebook AI Research, is a high-performance local library ideal for fast similarity search and prototyping.

2.Chromais a lightweight, open-source vector database built for LLM workflows and integrates smoothly with LangChain.

3.Pineconeis a fully managed cloud platform offering high scalability, enterprise-grade performance, and production-ready infrastructure.

4.Weaviateis an open-source vector database with both local and cloud deployment options, featuring GraphQL APIs, hybrid search, schema design, and strong multimodal capabilities. Together, these platforms cover everything from local experimentation to real-world AI deployment at scale.

Course Description

The rise of Generative AI and LLMs has madevector databases the new backbone of intelligent applications. Instead of searching by keywords, vector databases enablesemantic search— retrieving results based on meaning and context. This course takes you from themathematical foundations of embeddingsall the way tobuilding real-world AI apps using FAISS, Chroma, Pinecone, and Weaviate.

You'll learn how embeddings work, how Approximate Nearest Neighbor (ANN) algorithms power high-speed search, and how to design production-ready Retrieval-Augmented Generation (RAG) pipelines with LLMs. By the end of the course, you'll know exactlywhich vector database to use, when, and why— and how todeploy AI search systems at scale.

No outdated theory — this ishands-on, industry-grade contentdesigned for modern AI engineers, ML/LLMOps teams, full-stack developers, and ambitious learners.

What You'll Learn (Learning Objectives)

Understand how vector databases work and why they are core to AI search and RAG systems

Generate and evaluate embeddings using OpenAI, Hugging Face, & Python

Implement ANN search and compare indexing strategies

Build vector indexes using FAISS, Chroma, Pinecone, and Weaviate

Create semantic and multimodal search engines from scratch

Integrate vector DBs with LangChain and LLM APIs

Design and deploy full RAG pipelines with real data

Optimize query speed, memory usage, and scalability

Understand trade-offs between open-source and cloud vector DBs

Build production-grade AI applications for real clients

Who This Course Is For

Data scientists and machine learning engineers working with embeddings or RAG pipelines

Software/backend/full-stack engineers building chatbots or AI search systems

Data engineers and MLOps professionals managing AI infrastructure

NLP practitioners focused on similarity and context retrieval

Researchers exploring high-dimensional search or ANN algorithms

AI startup founders & product managers planning to integrate vector search

Hackathon participants or builders prototyping AI tools

Anyone aiming to master thedata layer behind modern generative AI

Vector Databases in Action: FAISS, Pinecone, Chroma & Weaviate - Course Curriculum

Module 1: Linear Algebra Foundations

Lecture 1:Linear Algebra Basics(Vectors, matrices, dot product, cosine similarity, vector norms, and their role in embeddings)

Module 2: Probability & Statistics for Vector Search

Lecture 2:Probability & Statistics for Vector Search(Distributions, similarity measures, distance metrics, and statistical intuition for high-dimensional search)

Module 3: Optimization & ANN Concepts

Lecture 3:Optimization & Approximate Nearest Neighbor (ANN) Concepts(Gradient descent, loss functions, dimensionality reduction, and ANN algorithms such as HNSW, IVF, PQ)

Module 4: Hands-on Python Math Labs

Lecture 4:Python Math Labs for Vector Search(NumPy-based linear algebra, similarity computations, and visualization of embedding spaces)

Module 5: Vector Database Foundations

Lecture 5:Introduction to Vector Databases(Concepts, architecture, storage, and retrieval mechanisms)

Module 6: Working with Embeddings

Lecture 6:Generating and Using Embeddings(Creating embeddings using OpenAI, Hugging Face, and sentence-transformers; storing and querying)

Module 7: FAISS (Facebook AI Similarity Search)

Lecture 7:FAISS Overview and Setup

Lecture 8:Indexing and Searching with FAISS

Lecture 9:Building a Semantic Search Engine with FAISS

Module 8: Chroma — Open-Source Vector DB

Lecture 10:Introduction to Chroma

Lecture 11:Creating and Managing Collections

Lecture 12:Using Chroma with LangChain and LLMs

Module 9: Pinecone — Managed Cloud Vector DB

Lecture 13:Overview of Pinecone

Lecture 14:Index Creation and Querying

Lecture 15:Building a Semantic Search Pipeline in Pinecone

Module 10: Weaviate — Open-Source Vector DB with Cloud Option

Lecture 16:Introduction to Weaviate

Lecture 17:Schema Design, Data Ingestion, and Querying

Lecture 18:Hybrid Search and GraphQL API

Module 11: Comparing Vector Databases

Lecture 19:Comparing FAISS, Chroma, Pinecone, and Weaviate(Performance, scalability, pricing, and ecosystem trade-offs)

Module 12: Real-World Projects

Lecture 20:Project 1 — Building a RAG Pipeline with LLMs and Vector DBs

Lecture 21:Project 2 — Image Similarity Search

Lecture 22:Project 3 — Knowledge Base Chatbot with Pinecone

Real-World Projects You'll Build

Semantic Search Engine with FAISS

RAG Pipeline with LLMs & Pinecone

Knowledge Base Chatbot Using LangChain

Image Similarity Search System

Performance Comparison Across Vector DBs

Hands-on Deployment & Optimization

By the End of This Course.

You'll be able to confidently design, choose, build, and deployAI-native search and RAG systemsusing industry-leading vector databases — just like the systems powering ChatGPT, Midjourney, Notion AI, and Google Gemini.

Ready to master one of the most important skills in AI today?Enroll now — and start buildingsemantic search, multimodal AI, and intelligent applicationswith vector databases.

Who this course is for:
- Data scientists and machine learning engineers working with embeddings, RAG pipelines, and AI search.
- Software and backend engineers building chatbots, semantic search, or recommender systems.
- Data engineers and MLOps professionals managing AI infrastructure and vector-based retrieval systems.
- AI engineers integrating vector search into LLM-based applications.
- NLP practitioners interested in improving context retrieval and text similarity.
- Full-stack developers wanting to implement RAG or hybrid search in their apps.
- Professionals transitioning from traditional databases to AI-native data systems.
- Students and researchers in AI, computer science, or data science who want real-world skills in modern AI storage.
- Researchers exploring semantic similarity, ANN algorithms, and high-dimensional search.
- Researchers working on multimodal search (text, image, audio similarity).
- Tech enthusiasts curious about how ChatGPT-style systems retrieve relevant information.
- Hackathon participants or AI builders creating prototypes involving vector search.
- Engineers evaluating performance trade-offs across different vector DB technologies.
- AI startup founders or product managers assessing vector DB integration in production systems.
- Developers looking to gain hands-on experience with FAISS, Pinecone, Chroma, and Weaviate.
- Anyone aiming to master the data layer behind modern generative AI and search systems.
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